Journal ArticleDOI
Hidden Markov Models in Bioinformatics
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TLDR
This survey considers the major bioinformatics applications ofHidden Markov Models, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition, and provides a critical appraisal of the use and perspectives of HMMs.Citations
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Journal ArticleDOI
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics
TL;DR: How the availability of high-throughput sequencing technologies has transformed microbiology and bioinformatics and how to tackle the inherent computational challenges that arise from the DNA sequencing revolution is reviewed.
Journal ArticleDOI
A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments
TL;DR: Proposed cybersecurity framework uses Markov model, Intrusion Detection System (IDS), and Virtual Honeypot Device (VHD) to identify malicious edge device in fog computing environment and results indicated that proposed cybersecurity framework is successful in identifying the malicious device as well as reducing the false IDS alarm rate.
Journal Article
Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models
Jüri Lember,Alexey Koloydenko +1 more
TL;DR: A careful analysis of a family of algorithmically defined decoders aiming to hybridize the two standard ones was proposed elsewhere, and several problems and issues with it and other previously proposed approaches are identified, and practical resolutions of those are proposed.
Proceedings ArticleDOI
Markov chain existence and Hidden Markov models in spectrum sensing
TL;DR: This paper validated existence of a Markov chain for sub-band utilization by PUs over time using real-time measurements collected in the paging band (928–948 MHz) and formulated a spectrum sensing paradigm as a Hidden Markov model that predicts the true states of a sub- band.
Journal ArticleDOI
An Intelligent Information Forwarder for Healthcare Big Data Systems With Distributed Wearable Sensors
TL;DR: It is shown that the intelligent forwarders can provide the remote sensors with context-awareness and transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Journal ArticleDOI
Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes
TL;DR: A new membrane protein topology prediction method, TMHMM, based on a hidden Markov model is described and validated, and it is discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C-in topologies.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.